International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMP...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Vol...
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50120130406014

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 6, November - December (2013), pp. 127-135 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME A TWO PHASE ALGORITHM FOR FACE RECOGNITION IN FREQUENCY DOMAIN Ms Archana H. Sable, Dr. Girish V. Chowdhary School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded(M.S.),India ABSTRACT Various changes in illumination, expression, viewpoint, and plane rotation present challenges to face recognition. Low dimensional feature representation with enhanced discrimination power is of paramount importance to face recognition system. In this paper, we propose a two-phase algorithm for face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectrums are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. The algorithm works in two phases: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. The experimental results using FERET, ORL(case-I), and ORL(case-II) databases are also presented and compared the two-phase face recognition method based on space domain of face images. Keyword: Face Recognition, DCT, DFT. 1. INTRODUCTION Face recognition technology has a variety of potential applications such as security access control, personal identification to human-computer communication, intelligent human machine interface, and so on. In the last few years, a number of face recognition and speech recognition methods have been proposed using transform domain such as principal component analysis(PCA) 127
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME [1],[2],[3],[4], linear discriminant analysis(LDA) [5][6], locality preserving projection (LPP) [7], [8], DCT[9], DFT[10] and discrete wavelet transforms(DWT) [11], [12]. A good many of DCT-based and DFT-based face recognition methods have been proposed. In [13], Jiang et al. investigated that a certain number of DCT coefficients are removed, the corresponding facial image description by the remaining DCT coefficients are robust to lighting changes and scale variations. Such good properties would be very useful for applications of face recognition, video object tracking, object segmentation and visual content processing. In general, there are two kinds of face recognition methods based on DCT and DFT. One only uses part of DCT coefficients or DFT amplitude spectrums for face recognition. For example, Sanderson et al.[14] used the DCT coefficients of neighboring blocks to generate an efficient feature for face recognition. Ghanbari [17] proposed an efficient feature extraction method based on DCT pyramid for face recognition. And the other based on DCT and DFT face recognition methods is holistic and uses all features of DCT or DFT to perform classification. For instance, Er. et al. [15] demonstrated an efficient method for high-speed face recognition based on DCT and radial basis function (RBF) neural networks. In [16], they presented a technique for recognition of frontal human faces on gray scale images. In this technique, the distance between the DCT of the face under evaluation and all the DCTs of the faces database are computed. Jadhao.et at[18] used Radon transform and Fourier transform for face recognition on ORL database and achieved a good performance. Furthermore, more face recognition algorithms focusing on selection of efficient features from DFT or DCT can be found in [19], [20], [21], [22], [23]. Specifically, 3D face recognition techniques based on 3DDCT features and 3D frequency-domain representation can be found in [24], [25],[26]. Theoretically, transform-based approaches have a number of practical advantages. First, they have been proven to be very efficient with face images of different features and illumination. Second, they have been proven to be good with face images of different scales, poses and rotated images. Third, they may be more efficient than in the pixel domain. Recently, a method that addresses classification problems from a novel viewpoint has been proposed. This method uses a two-phase test sample representation method to perform face recognition [27]. In this method, the first phase represents a test sample as a linear combination of all the training samples, and exploits the representation ability of each training sample to determine the K nearest neighbors for the test sample. The second phase represents the test sample as a linear combination of all the K nearest neighbors and uses the representation result to perform classification. We note that the above method is a successful 2 norms based sparse representation method. Though it obtains a very high accuracy, it is computationally more efficient than the naïve 1 norm-based sparse representation method proposed in[28]. Moreover, the 2 norms based sparse representation methods not only can perform well in face recognition [29], [30], [31], [32], but also achieve a good performance in palm print recognition [33] and bimodal biometrics [34]. Furthermore, the 2 norms based sparse representation methods have been extended into the feature space [35]. The method proposed in this paper is holistic and uses all the DCT coefficients or the DFT amplitude spectrums to generate the transformed images. Firstly, we apply the Euclidean distance formula to measure the distance between a test sample and each sample in the training sets, and then exploit the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Then, we represent the test sample as a linear combination of the determined K nearest neighbors and use the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectrums to test the effect on performance of our algorithms. The experimental results demonstrate that our proposed algorithms are very competitive. 128
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME This paper is organized as follows. Section 2 describes the structure of the proposed method for face recognition. Experimental results are presented and discussed in Section 3. Finally, conclusions are drawn in Section 4. 2. THE PROPOSED ALGORITHM The proposed method is divided into four stages: feature selection, the first phase of test sample representation, the second phase of test sample representation and classification. In feature selection stage, all the samples are converted into feature matrices by implying DCT or DFT. Then, we extract the absolute values of DCT coefficients or DFT amplitude spectrums. Furthermore, we convert each feature matrix into a matrix with the same size as the matrices of the original images, which formed transformed images. a b Fig.1. Result of DCT on a sample from the ORL database (a) original image, (b) DCT image Fig.2. Result of DFT on a sample from the ORL database (a) original image, (b) DFT image. Figs.1 and 2 show the application of the DCT and DFT on one of the face images obtained from the ORL database. Fig.1.a and Fig.2.a display the original image. Fig.1.b and Fig.2.b display the result of applying the DCT and DFT on the original image, respectively. In the following sections, we assume that there are L classes and n training samples x1,…, xn. If a sample is from the j th class j =1,2,…,L, we take j as the class label of the training sample. In the   first phase of the test sample representation, first of all, we use the Euclidean distance formula to measure the distance between a test sample and all the training samples. The Euclidean formula for distance in d dimensions is: d D(a.b )=  ∑   n =1 1/ 2 2 (an −bn )  (1)  Where, a andb are the column vectors of samples. Secondly, the Euclidean distance between a test sample y and the i th training sample will be D( y, xi)(i=1,…, n) . A smaller D( y, xi) means that the i th training sample is more similar to the test sample than other training samples, and which has a great contribution in representing the test sample. Furthermore, we exploit D( y, xi)(i=1,…, n) to identify the K training samples that have the K minimal distances, and denote them by x1…,xk . The test sample can be represented by x1…,xk. And these samples are considered as the K nearest neighbors of the test sample. In addition, let 129
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME C ={ c1…,cd}, a set of some numbers, stand for the set of class labels of the K nearest neighbors. C are one subset of set={ 1,…,L}In the second phase of the test sample representation, firstly, we represent the test sample as a linear combination of the determined K nearest neighbors. Accordingly, we assume that the following equation is approximately satisfied: ~ ~ y = b1 x1 + ... + b K x K (2) ~ Where xi (i=1,..,K) are the identified K nearest neighbors, and bi (i=1,…,K) are the coefficients. Equation (2) shows that the K nearest neighbors of test sample makes their own contribution to representing the test sample, which are the best representing of the test sample. If the dimensions of ~ xi (i=1,..,K) are d , the time complexity of Equation (2) will be O(d3 ) . Secondly, according to the ~ ~ neighbors might be from different classes, all the neighbors from the r th ( r єc ) class are x s ,..., xt , then the sum of the contributions to represent the test sample of the r th class will be ~ ~ g r = bs x s + ... + bt xt (3) In classification stage, first, we convert a test image into a transformed image ty by using the features selection method, and then we calculate the deviation of gr from ty by using D r = || t y − g r || 2 , r ∈ c (4) A smaller deviation Dr means a greater contribution to representing the test sample. Thus, we classify ty into the class that produces the smallest deviation. As a result, we use the Euclidean distance formula instead of the linear combination to determine the K nearest neighbors of the test sample in the first stage. Thus, our proposed algorithms have a lower time complexity than TPTSR. 3. EXPERIMENTAL RESULTS In order to test the efficiency of our algorithms presented above, we perform a series of experiments using the ORL [36] and the FERET [37], [38] face databases. The ORL face database contains 400 images of 40 persons (10 images per person). The resolution of ORL images is 46×56. The FERET face database contains 1400 images of 200 persons (7 images per person). The resolution of FERET images is 40×40. Both of these databases, the images that belong to the same person usually present variations in expression and illumination. Sample images from the two databases are displayed in Figs 3 and 4. Fig.3. Some Face Images of a Person from the ORL Database 130
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Fig.4 Some Face Images of a Person from the FERET Database If s samples of the n samples per class are used for training and the remaining samples are used for testing, there are C ns = n ( n − 1)...( n − s + 1) s ( s − 1)... 1 possible combinations. As a result, there are Cns training sets and Cns test sets. For the ORL database, we test different algorithms in two cases. In the first case, we use five images of each person as the training samples and there are 252 training and test sample sets. In the second case, we use six images of each person as the training samples and there are 210 training and test sample sets. For the FERET database, we use four images of each person as the training samples and take the remaining images as test samples. As a result, we test different methods by using 35 training and test sample sets from the FERET database. When implementing our method, we solve B by using ~ ~ ~ B = X T X + γI ) −1 X T Y . In order to eliminate the influence of the negative values, we use the absolute values of DCT coefficients or DFT amplitude spectrums. Beyond that, γ is set to 0.001. 3.1 Performance comparison This section compares our proposed algorithms performances, including the two-phase test sample representation method based on DCT (TPTSR-DCT), the two-phase test sample representation method based on DFT (TPTSR-DFT), two-phase face recognition method based on DCT (TPFR-DCT), two-phase face recognition method based on DFT (TPFR-DFT), with four existing algorithms, namely, TPTSR[27], SRC[28], PCA, LDA. The algorithms of TPTSR-DCT and TPTSR-DFT use the linear combination of training samples to determine K neighbors. The algorithms of TPFR-DCT and TPFR-DFT use the Euclidean distance between the test sample and the training samples to determine the K neighbors. Fig.5 Means of the rates of the classification errors (%) of our algorithms (TPTSR-DCT,TPFR-DCT, TPTSR-DFT, TPFR-DFT), TPTSR on the ORL face database (the first case) 131
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Fig.6 Means of the rates of the classification errors of our algorithms (TPTSR-DCT,TPFR-DCT, TPTSR-DFT, TPFR-DFT), TPTSR on the ORL face database (the second case) Fig.7 Means of rates of the classification errors of our algorithms (TPTSR-DCT,TPFR-DCT, TPTSR-DFT, TPFR-DFT), TPTSR on the FERET face database Figs.5, 6 and 7 shows the means of rates of the classification errors (%) of our algorithms and TPTSR for the ORL database (the first case), the ORL database (the second case) and FERET database, respectively. Figs.5 and 6 clearly show that our proposed algorithms are always able to obtain a much lower means of rates of the classification errors than TPTSR. Fig.7 shows the means of rates of the classification errors of our proposed algorithms are lower than TPTSR in most cases. But, the performances of TPTSR-DFT and TPFR-DFT are inferior to TPTSR when the number of K is smaller certain values on FERET face database. In most cases, the performances of TPTSR-DCT are better than TPFR-DCT on the ORL and FERET face databases. As for TPFR-DFT and TPTSRDFT, the performances of TPFR-DFT are better than TPTSR-DFT in most cases on ORL and FERET face databases. Most important of all, those figures also show that our proposed algorithms can achieve a better performance than TPTSR when they use a suitable K nearest neighbors training samples to represent the test sample. Moreover, it can be observed than there is an increase in the means of the rates of the classification errors as the numbers of K increase. But if we use a suitable K nearest neighbors training samples to represent the test sample, the means of the rates of the classification errors of our algorithms can achieve below 2% and 25% for ORL and FERET face databases, respectively. 132
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Table.1. Mean of the rates of the classification errors (%) of Global version of our algorithms (TPTSR-DCT, TPFR-DCT, TPTSR-DFT, TPFR-DFT), TPTSR, SRC, PCA, LDA TPTSR DCT TPFR DCT TPTSR DFT TPFR DFT TPTSR LDA SRC PCA LDA ORL-Case I 3.7 3.7 2.82 2.82 6.8 5.18 10.2 4.8 ORL-caseII 2.8 2.8 2.11 2.11 5.6 3.3 8.2 3.7 FERET 2.5 2.5 3.23 3.23 4.21 2.59 38.3 3.63 Table.1 shows the means of the rates of the classification errors of the global version of our proposed algorithms, the global version of TPTSR, SRC, PCA and LDA in the ORL (the first case and the second case) and FERET databases. The global version is the implementation of our proposed algorithms in the case where K is equal of the total number of training samples. That is, in the first stage of our algorithms, the test sample is represented by all the training samples. The global version of K for the ORL and FERET face databases is 200 and 800, respectively. In PCA algorithm, the rest of the feature vectors contain information account for 90% of total amount of information. For the LDA algorithm, the transform axes of feature extraction on ORL database and FERET database are 39 and 199, respectively. Table.1demonstrates that the performances of our proposed algorithms are better than the TPTSR, SRC, PCA and LDA. Moreover, TPTSR-DCT and TPFR-DCT achieve the best results on the FERET database. The performances of TPTSR-DFT and TPFR-DFT are the best on the ORL database (the second case). The reasons may be that the DCT coefficients and DFT amplitude spectrums are more robust to lighting changes and scale variations than pixel gray values, which can improve the face recognition performances. In addition, the test sample and its nearest neighbors usually belong to the same class, so our algorithms provide a very reasonable way to determine the class labels. Most important of all, our algorithms are an extended of TPTSR, which is a supervised sparse representation method. Therefore, our algorithms have all the advantages of the SRC algorithms, and achieve a better performance than the TPTSR, SRC, PCA and LDA. REFERENCES [1] [2] [3] [4] [5] [6] J.Yang, D. Zhang, A. F. Frang, J.-Y. Yang, Two-dimensional PCA: A new approach to appearance-based face representation and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(1), 131-137(2004). Y. Xu, D. Zhang, J. Yang, Z. Jin, J.-Y. Yang, Evaluate dissimilarity of samples in feature space for improving KPCA, International Journal of Information Technology & Decision Making. 10(3), 479-495(2011). A. Pentland, B.Moghaddam, T.Starner, View-based and modular eigenspaces for face recognition, IEEE Conference on Computer Vision and Pattern Recognition. pp.84-91(1994). P.Ma, D.Yang, Y.Ge, X.Zhang, Y.Qu, Face recognition using two-dimensional nonnegative principal component analysis, Journal of Electronic Imaging. 21(3),doi:10.1117. Z. Fan, Y. Xu, D. Zhang, Local linear discriminant analysis framework using sample neighbors, IEEE Transactions on Neural Networks. 22(7), 1119-1132(2011). C-Y. Chang, C-W. Chang, C-Y. Hsieh, Applications of Block Linear Discriminant Analysis for Face Recognition, Journal of Information Hiding and Multimedia Signal Processing. 2(3), 259-269(2011). 133
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  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME [26] G. Gunlu, S.B.Hansan, Feature extraction and discriminating feature selection for 3D face recognition, 24th International Symposium on Computer and Information Sciences. pp.44-49 (2009) [27] Y. Xu, D. Zhang, J. Yang, J-Y Yang, A two-phase test sample sparse representation method for use with face recognition, IEEE Transactions on Circuits and systems for video technology. 21(9), 1255-1262 (2011). [28] J. Wright, A.Y. Yang, A. Ganesh, et. Al., Robust face recognition via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence.31(2),210-227(2009). [29] Y. Xu, W. Zuo, Z. Fan, Supervised sparse presentation method with a heuristic strategy and face recognition experiments, Neurocomputing.79, 125-131(2011). [30] Y. Xu, Q. Zhu, A simple and fast representation-based face recognition method, Neural Computing and Applications. DOI: 10.1007/s00521-012-0833-5. [31] L.Zhang, M.Yang, X. Feng, Sparse representation or collaborative representation: which helps face recognition?, ICCV. pp.471-478(2011). [32] Y.Xu, Q. Zhu, D. Zhang, J-Y. Yang, combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palm print recognition experiments, Neurocomputing. 74, 3946-3952(2011). [33] Q. Shi, A. Eriksson, A. Hengel, C. Shen. Is face recognition really a compressive sensing problem? 2011 IEEE Conference on Computer Vision and Pattern Recognition. pp.553-560 (2011). [34] Y. Xu, A. Zhong, J.Yang, D. Zhang, Bimodal biometrics based on a representation and recognition approach, Optical Engineering. 50(3), 037202-037202-7(2011). [35] Y.Xu, Z.Fan, Q.Zhu, Feature space-based human face image representation and recognition, Optical Engineering. 51(1),017205-017205-8 (2012). [36] [online]Available:http://www.cl.cam.ac.uk/research/dtg/attarhive/facedatabase.html. [37] P.J.Phillios, H. Moon, S.A. Riziv, P.J. Rauss, The FERET evaluation methodology for facerecognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(10), 1090-1104(2000). [38] P.J.Phillios, The facial recognition technology (FERET) database [online], available: http://www.itl.nist.gov/iad/humanid/feret/feret_master.html. [39] P. Prasanth Babu, L.Rangaiah and D.Maruthi Kumar, “Comparison and Improvement of Image Compression using Dct, Dwt & Huffman Encoding Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 54 - 60, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [40] Sambhunath Biswas and Amrita Biswas, “Fourier Mellin Transform Based Face Recognition”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 8 - 15, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [41] Prof. B.S Patil and Prof. A.R Yardi, “Real Time Face Recognition System using Eigen Faces”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 72 - 79, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [42] Steven Lawrence Fernandes and Dr. G Josemin Bala, “Analysing Recognition Rate of LDA and LPP Based Algorithms for Face Recognition”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 115 - 125, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [43] U.K. Jaliya and J.M. Rathod, “A Survey on Human Face Recognition Invariant to Illumination”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 517 - 525, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [44] S. K. Hese and M. R. Banwaskar, “Appearance Based Face Recognition by PCA and LDA”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 48 - 57, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. 135

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